Modular RL methods for ObjectNav have also emerged as strong competitors to end-to-end RL [1]}, [2]}. They rely on individual modules for semantic mapping, high-level semantic exploration (i.e., where to look?), and low-level navigation (i.e., how to navigate to \((x, y)\) ?). The semantic exploration module is learned through RL, yet it is more sample-efficient and generalizes better than end-to-end RL due to modularity and shorter time horizons. We propose a novel strategy for the semantic exploration module. Specifically, we decide `where to look?' using a potential function network that is trained non-interactively via supervised learning on a dataset of semantic maps. When integrated into a state-of-the-art modular pipeline [1]}, our method achieves better performance while incurring significantly lower computational cost for training.
